Much work in the field of image processing is directed to automatic processing of images to accurately and reliably detect objects of interest with little or no human intervention. Images can include an immense amount of information. For example, a single color or even grayscale digital image may include millions of pixels, each including information about an associated region of the image. The pixels collectively portray imaged objects that generally give rise to variations in contrast (e.g., a grayscale image). Humans have an ability to inspect images and to readily identify areas of interest, often with minimal effort. Fortunately, human interpretation allow's for rapid processing of vast amounts of information, aided by an ability to quickly form certain judgments, relying on prior knowledge, adding inferences, etc. Automated or machine interpretation can attempt to approximate such a process through expert systems, artificial intelligence, and the like, but typically, at great expense and complexity.
Certain approaches in image processing are directed toward reducing the amount of information necessary for processing, thereby simplifying the task of identifying areas of interest. It is well known that objects portrayed in an image usually give rise to variations in contrast. Certain features of such objects, such as an edge or boundary, can result in areas in the image having particularly strong intensity contrasts at which there may be a jump in intensity from one pixel to the next. Since edges tend to characterize boundaries, techniques for detecting edges are often used to significantly reduce the amount of data, for example, filtering out useless information, while preserving the important structural properties in an image.
Several popular techniques are available to automatically detect edges directly from the image. Such techniques generally rely on spatial differentiation of the image intensity function. The result produces a collection of essentially one-dimensional edges. In the field of feature detection, edges often serve as a starting point. Since edges determined by such techniques generally do not form completely connected boundaries of objects, it is quite often difficult, if not impossible, to determine any reasonable association of the edges to respective objects. Some techniques attempt to identify corners, using such information to connect edges forming shapes. Even with such techniques, it is often difficult to identify the presence of an object from one-dimensional edges alone.
Prior art methods for detecting objects of interest in images are often unable to distinguish between the object of interest and its surroundings. Typical edge detection methods fail because edges that form on unwanted features (i.e., surroundings) are often stronger in intensity than those formed on the object of interest in the image. A need therefore exists for improved systems and methods for detecting objects in an image.